A region‐based segmentation method for ultrasound images in HIFU therapy. Issue 6 (19th May 2016)

• Record Type: Journal Article
• Authors: Zhang, Dong; Liu, Yu; Yang, Yan; Xu, Menglong; Yan, Yu; Qin, Qianqing
• Is Part Of: Medical physics. Volume 43:Issue 6(2016)Part 1
• Page Start: 2975
• Page End: 2989
• Publication Date: 2016-05-19
• Subjects: biomedical ultrasonics -- cancer -- feature extraction -- feature selection -- image segmentation -- image texture -- iterative methods -- medical image processing -- pattern clustering -- speckle -- tumours -- ultrasonic imaging -- ultrasonic therapy; Ultrasonography -- Treatment planning -- Cancer -- Segmentation -- Numerical simulation; solution of equations; Diagnosis using ultrasonic, sonic or infrasonic waves -- Ultrasound therapy -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture; HIFU therapy -- ultrasound image segmentation -- superpixels -- gray level co‐occurrence matrix (GLCM) -- multiway spectral clustering; Cancer -- Ultrasonography -- Medical image segmentation -- Cluster analysis -- Cluster spectra -- Ultrasound therapy -- Medical image noise -- Eigenvalues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4950706
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound‐guided high‐intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient's body. Therefore, the segmentation becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region‐based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region‐based split‐and‐merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel‐based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first‐order and second‐order texture features computed from the gray levels and the gray level co‐occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. Results: To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area‐based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance‐based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318 × 333 (pixels) image. Conclusions: Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real‐time guidance in HIFU therapy.